Heinz Koeppl

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 57 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Reinforcement Learning — 13 citations into it; #1,641 of 2,242 by trust here, #2,092 by citations
• Model Architectures — 8 citations into it
• Biophysics — 6 citations into it

Whom Heinz Koeppl trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Andrew Y. Ng
3. Andrew L. Maas
4. Pieter Abbeel
5. Geoffrey E. Hinton
6. Ross B. Girshick
7. Alex Krizhevsky
8. Ilya Loshchilov
9. Richard S. Sutton
10. Yoshua Bengio

Who trusts Heinz Koeppl overall

1. Maximilian Hüttenrauch

Papers from Heinz Koeppl's perspective

Ordered by how strongly the researchers Heinz Koeppl trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Et al
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Inferring causal molecular networks: empirical assessment through a community-based effort (2016) — 222 citations
• Attention-Based Transformers for Instance Segmentation of Cells in Microstructures (2020) — 110 citations
• Inverse Reinforcement Learning in Swarm Systems (2016) — 82 citations